System and method for determining and using knowledge about human health

ABSTRACT

A system and method for predicting a health outcome of a user based on a determination of knowledge the user possesses regarding issues of physiological or mental health.

TECHNICAL FIELD

Examples described herein relate to a system and method for determiningknowledge about human health.

BACKGROUND

Online services exist which provide interactive gaming and socialenvironments for users. These services generally exist for amusementonly.

There also exists a questionnaire, termed the Patient Activation Measure(“PAM”), provided by Insignia Health under license from the State ofOregon, which includes a static set of questions that areknowledge-based and deemed correlative to health.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for predicting a physiological or mentalhealth of a user based on the user's knowledge level of health,according to one or more embodiments.

FIG. 2 illustrates an analysis system, according to an embodiment.

FIG. 3 illustrates an example of a data structure that can be developedto link a question with a health outcome and a topic, according to oneor more embodiments.

FIG. 4 illustrates an example method for predicting a health outcome ofa user based in part on whether a user has independent knowledge of anassertion relating to health.

FIG. 5 illustrates an example method for predicting a health outcome ofa user based on a knowledge profile of a user.

FIG. 6A illustrates an example method for providing a health relatedservice to a user based on a knowledge-predicted health outcome for auser.

FIG. 6B illustrates a health service sub-system 680, according to anembodiment.

FIG. 7A illustrates an example method for providing a game-basedenvironment in which user responses enable prediction of health outcomesfor individual users.

FIG. 7B illustrates a knowledge-based recommendation engine, accordingto one or more embodiments.

FIG. 8A through 8H illustrate example interfaces for use with one ormore embodiments described herein.

FIG. 9 is a block diagram that illustrates a computer system upon whichembodiments described herein may be implemented.

DETAILED DESCRIPTION

Some embodiments include a system and method for predicting a healthoutcome of a user based on a determination of knowledge the userpossesses regarding issues of physiological or mental health.

Still further, in some embodiments, a system and method is provided forproviding a health service benefit to a user based on their predictedhealth, as determined from the user's knowledge of human health.

In one embodiment, a collection of assertions are stored in which eachassertion pertains to human health. For each individual in a controlpopulation of persons, a value of a predetermined health parameter isdetermined which is indicative of that person's health. For eachassertion of the collection, a correlative health parameter isdetermined which is indicative of an association between thoseindividuals in the control population that have independent knowledge ofthe assertion and the value of the predetermined health parameter forpersons of the control population. The collection of assertions can bestored by associating each assertion with the determined correlativehealth parameter for that assertion. An interface is provided for a userto indicate the user's independent knowledge of each assertion in atleast a subset of assertions from the collection. A health outcome ispredicted for the user based at least in part on the correlative healthparameter of individual assertions in the subset of assertions.

In still another embodiment, a health outcome of a user is predictedbased on a knowledge profile determination of the user. In oneembodiment, a knowledge profile is determined for the user whichreflects the user's independent knowledge of individual assertions in acollection of assertions. A correlation is determined as between a setof facets of the user's knowledge profile and a corresponding set offacets of multiple individual person's knowledge profile. The knowledgeprofile can be determined for at least a set of assertions from thecollection of assertions. A health outcome is determined for each of themultiple individual persons. The health outcome of the user can then bepredicted based in part on the correlation and the health outcome ofeach of the multiple individuals.

In still another embodiment, a knowledge profile is determined for theuser to reflect the user's independent knowledge of individualassertions in a collection of assertions. Each assertion in thecollection can be non-specific to the user or to any person of thepopulation, but otherwise known to be correlative to human health. Adetermination is made as to a first correlation value as between theknowledge profile of the user and a knowledge profile of a control groupof persons for whom one or more health outcomes are known. A firsthealth outcome is predicted for the user based on the first correlationvalue. A health service benefit is provided to the user based at leastin part on the predicted health outcome.

Still further, according to another embodiment, a human health knowledgeprofile is determined for each user in a group of users, the humanhealth knowledge profile reflecting that user's independent knowledgeabout assertions in a collection of assertions. Each assertion in thecollection of assertions may pertain to human health and is non-specificas to any user or to any person of the population. At least a firstcorrelation value is determined as between a facet of the knowledgeprofile of individual users in the group of users and a correspondingfacet of the knowledge profile of a control group of persons for whomone or more health outcomes are known. A subset of one or more users isselected based on the first correlation value of each user of the subsetexceeding a threshold designation. A service or designation is providedfor a set value to the one or more users of the subset, and not to otherusers of the group. The service or designation may be associated with atrue per-user cost that is not equal to the set value, but which isvariable and set to increases over time when individual users in thesubset suffer negative health consequences as a result of a naturallyprogressing medical condition. Still further, some embodiments include asystem and method for providing a health service or benefit to a user.By way of examples a health service or benefit can include healthinsurance (including primary or supplemental), life insurance,enrollment in a facility to receive medical attention, medicalpublications, as well as discounts or augmented services thereof. In oneembodiment, a collection of questions are stored, where each question isbased on a documented assertion pertaining to human health. Eachquestion in a first subset may be associated with a correlative healthparameter that is based at least in part on (i) persons in a controlpopulation of that have independent knowledge of an assertion that is abasis of that question, and (ii) a value of a predetermined healthparameter for each person in the control population the value of thepredetermined health parameter for each person being indicative of thatperson's health. Additionally, the second subset of the questions isassociated with a null (i.e. non-existent) or neutral (i.e., notindicative of health) correlative health parameter. A corresponding setof questions is displayed to the user from the collection for responsefor each user in the set of users. A response score is determined foreach user in the set of users based on a correctness of their respectivereply to each question in the corresponding set of questions. A healthparameter value is determined for at least a health outcome based atleast in part on the correlative health parameter of at least somequestions in the corresponding set of questions.

Still further, some embodiments include a system and method forproviding health recommendations to a user. In an embodiment, aplurality of questions are provided to the user. The plurality ofquestions can include multiple questions for each of multiplehealth-related topics, so that individual questions are each associatedwith one or more of the multiple topics. A score is determined for theuser in answering each question in the plurality of questions. The scorecan include topical scores for one or more of the multiple topics. Basedon the topical score of at least a first topic, a set of recommendationscan be identified for the user. The set of recommendations may includean action that the user can perform to improve the user's mental orphysiological health relating to the topic.

While examples such as described are implemented on computer systems,empirical data has been derived to show health outcome prediction can becorrelated to user's knowledge. For example, examples have determinedthat positive health outcome determinations made from evaluating user'sanswers directly correlate to fewer hospital stays.

System Overview

FIG. 1 illustrates a system for predicting a physiological or mentalhealth of a user based on the user's knowledge level of health,according to one or more embodiments. A system 100 as shown by anexample of FIG. 1 can be implemented using a combination of servers, orother computers which combine to provide a network service for clientcomputers operated by a user base. While an example of FIG. 1illustrates the system 100 being implemented as a combination of logicalcomponents, alternative implementations can readily provide forfunctionality described to be integrated or discrete. Moreover, specificcombinations of functionality and processes described can alternativelybe performed as sub-combinations or alternative combinations. Likewise,an example of FIG. 1 illustrates use of multiple data stores, which canlogically and/or physically be implemented as a combined or integrateddata structure (e.g., database), or alternatively, in distributedfashion such as shown.

Among other implementations, system 100 can be accessible to users 11over a network 101, such as the World Wide Web, to mobile computingdevices (e.g., feature phones, tablets, etc.), personal computers (e.g.,desktop computers, laptops, etc.) and other user operated computingdevices for purpose of interactively engaging individual users todetermine their knowledge level on various health topics, and furtherfor predicting the individual user's physiological or mental healthbased on their knowledge level of health. Among other advantages, anexample of FIG. 1 enables facets of physiological or mental health to bedetermined for a person, without need for obtaining user specificmedical information or biological samples. For example, in oneimplementation, a user's health can be predicted without use of anyuser-specific medical question. In a variation, a user's health can bepredicted based only on inputs of gender and age.

As described in greater detail, system 100 generates fact-basedquestions on various topics of health for purpose of (i) obtainingresponses from users, and (ii) correlating some of those responses tophysiological or mental health determinations. One of the underlyingassumptions of system 100 is that the living habits and behaviors ofpeople generally tends to have a measurable impact on theirphysiological or mental health, particularly when the assumption isapplied to a statistically significant sample of people (e.g., hundredsor thousands of persons). Under a statistically significant sample,embodiments described herein have recognized that a correlation can bemade as between the knowledge or awareness of individuals and theirrelative health outcome. More generally, embodiments recognize thathealth-conscious individuals are generally more knowledgeable abouthealth and also more healthy as compared to less healthy people (e.g.,individuals who suffer from obesity, heart disease, etc.). In fact,embodiments recognize that healthy individuals are significantly moreconscientious of maintaining healthy living habits and activities, andwith this mindset, such individuals are far more knowledgeable abouthealth than the rest of the population.

With this recognition, embodiments described herein provide a system forgauging how conscientious a given user is with respect to health, basedon the user's awareness of information that is specific and healthdriven embodiments further recognize that such. Such information, whichin many cases may qualify as trivia, nevertheless provides a mechanismfor delineating those individuals in the population who are in factconscientious about healthy living habits. Furthermore, embodimentsdescribed herein programmatically correlate knowledge of health tophysiologic health of individuals amongst a statistically significantsample size of users.

In order to gauge knowledge, an embodiment of FIG. 1 maintains a libraryof fact-based assertions on various subjects of human health, such asnutrition, exercise, medicine, etc. In an example of FIG. 1, theassertions are presented to users in the form of questions, for whichresponses can provide answers that are either correct or incorrect, andfurther enable evaluation of knowledge based on whether correct orincorrect answers were given by the users. While examples provide forassertions to be presented to users in the form of questions for purposeof violating their knowledge, other embodiments may use alternativeforms of interaction in order to gauge the user's awareness or knowledgeof a particular assertion. For example, the user may be provided astatement that is presented as an answer, and the interaction requiredof the user can be for the user to generate a question that yields theparticular answer. In this reverse format, the user's ability togenerate the question, combined with a statement as the presentedanswer, serves as a mechanism for determining whether the user hasindependent knowledge of the underlying assertion from which thestatement was originally presented.

Still further, as described in greater detail, some embodiments utilizea collection of assertions, of which only some have been determined tocorrelate to physiological or mental health. The user may have noknowledge of which questions correlate to health, or that only somequestions have direct correlation to health while others are beingprovided for alternative purposes (e.g. amusement). In some cases, theuser may even have no knowledge that some of the assertions for whichthe user is responding to have any correlation to do with their actualphysiological or mental health. Among other benefits, the use of manyquestions, in combination with questions that have been determined tocorrelate to physiological or mental health, preclude some individualsfrom ‘gaming’ the questions in a manner that masks their true knowledgelevel and awareness.

In more detail, system 100 includes a user interface 110, questionselection logic 120, response logic 130, and health scoring logic 140.The question selection 120 can receive or access questions 127 from aquestion library 152, and the user interface 110 can present contentbased on the selected questions 127 to individual users in any one of avariety of computing environments that stimulate the individual toprovide purposeful responses that reflect the user's understanding andknowledge for a topic of the question. The questions 127 can vary intheir purpose. In one example, question library 152 includes (i) a firstset of questions 127 a which have been correlated to physiological ormental health, and (ii) a second set of questions 127 b which have notbeen correlated to physiological or mental health, but which may servethe alternative purpose of providing trivia, factual information, and/orentertainment. Additionally, the questions of library 152 can beassigned to topics and sub-topics. Still further, the questions of thelibrary 152 can be associated with a difficulty score, based on, forexample, a correction rate amongst a control group of persons whoanswered the question.

When the user initiates a session, the user interface 110 may record auser ID 121 and session information 125. In implementation, the userinterface 110 can authenticate the user, and provide credentials 139 fora user profile store 138 in order to obtain profile data 137. Theprofile data 137 can identify, for example, any one or more of (i) thetopic that the user was previously being questioned on, (ii) a topic theuser is interested in, (iii) identifiers to questions that the user aspreviously answered, and/or (iv) a determined knowledge level 135 of theuser. With the profile data 137, the user interface 110 can identifyparameters or other information for facilitating question selections forthe user. In one example, the user interface 110 can use the profileinformation 137 to specify one or more topical parameters 123 and/or theknowledge level 135 of the user. In turn, the question selection 120 canselect questions 127 based on parameters 113, which can be based on, forexample, topic parameter 123, knowledge level 135, or user interestand/or preferences.

The profile data 137 can also include user-specific game data 119 (e.g.,user's personalizations for gaming, historical performance on games,current game play state, etc.). Additionally, the profile data 137 caninclude the user's community or social network data 117 (user'spersonalizations for community or social network application, socialnetwork content, etc.). The user-specific game data 119 and community orsocial network data 117 can, for example, be loaded through therespective functional layers of the user interface 110 when the userinitiates a session with a service of system 100.

The user interface 110 can be used to record responses 129 fromindividual users. In one implementation, each question 127 can becommunicated to the user interface using a sequence in which the answerto the question is also packaged and presented to the user. Someconditional logic may also be provided with the question 127, so that,for example, if the user response is correct, the user is instantlynotified and the next question is presented to the user. However, theconditional logic may render an alternative content in response toincorrect user response, specifically a panel or other information itemwhich provides information regarding the actual answer to the questionpresented. In this way, the user is made more knowledgeable.

The responses 129 can correspond to input that identifies, for example,the user's answer to a particular question. The responses 129 canidentify the answer of the user, the question that was answered, and anidentifier of the user. In some implementations, each question 127 canfurther be associated with one or more subject matter topics. Responselogic 130 can process the responses 129 from the various users. In oneimplementation, an initial determination of response logic 130 iswhether the question identified with response 129 is pre-associated witha physiological or mental health correlation, or whether no suchpre-association physiological or mental health correlation exists forthe question. In one implementation, the response logic 130 records acorresponding response entry 131 for each response, regardless ofwhether the question of the response has pre-association withphysiological or mental health. The response entry 131 can reflectwhether the answer to the question is correct, as well as the trueanswer. In some implementations, the response entry 131 further linksthe question answered to topical designations for the question, as wellas calibration or difficulty scoring.

Scoring logic 144 can process the answer of response entry 131 todetermine a score value 145 to associate with the particular recordentry. The score value 145 can be based in part on the difficulty levelof the question, which in some implementations, is provided as acalibration coefficient that is pre-associated with the question. Thus,the mathematical process to tabulate scoring can include factors such asthe number of questions the user correctly answered, the number ofquestions the user incorrectly answered, the difficulty parameterassociated with each question, and/or secondary considerations such asthe time it took for the user to provide the response and/or whether theuser correctly answered the question on the first try. The score value145 can be stored with the response data store 118.

Additionally, scoring logic 144 can also tally one or more aggregate oroverall scores for the user based on a historical record of responses.For example, the response data store 118 can maintain one or moreaggregate or ongoing subject matter topical scores (e.g.,weight-lifting), as well as an overall score for the user. As describedwith other examples, scoring logic 144 can be used to developcomparative scoring as between users, based on her overall knowledge,session performance, and/or topical subject matter knowledge.

The response logic 130 can optionally include a knowledge leveldetermination component 134. The knowledge level determination component134 can determine from the response 129 the knowledge level 135 of theuser. Alternatively, the knowledge determination component 134 candetermine the knowledge level of the user from the difficulty parameterassociated with the question and/or with the score output, as providedby the scoring component 144. The knowledge level determinationcomponent 134 can determine an overall knowledge level or atopic-specific knowledge level 135. The determined knowledge level(s)135 can be stored as part of the user profile 138, so that the knowledgelevel of the user is communicated to the questions selection logic 120when the user initiates a session with system 100.

For those selected questions which are identified as having apre-associated physiological or mental health correlation, the responselogic 130 can provide a corresponding health question record 133 whichidentifies, for example, the question, the answer provided, and/orwhether the question was answered correctly. The health question record133 can also specify a topic or topics of the question.

According to some embodiments, the question identified with the healthquestion record 133 can be associated with a health parameter value 151.As described by other examples, the health parameter value 151 can bedetermined as part of a correlative model that is developed using acontrol population in order to provide a quantified correlation tophysiological or mental health. A health scoring data store 150 canmaintain a collection of health parameter values 151 for individualquestions. In one implementation, the health parameter values 151reflect a predefined health outcome. Multiple health outcomes can beconsidered, and each question of health question record 133 can beassociated with a particular health outcome. By way of examples, thepossible health outcomes that have quantifiable correlations to thehealth parameter values 151 include (i) health care cost for anindividual in a given time period, (ii) number of medical facilityvisits by an individual in a given time period, (iii) number ofprescriptions that the person takes in a given time period, and/or (iv)number of sick days that the person took. Other examples of healthoutcomes include propensity for cancer (including cancer of differenttypes), heart disease, diabetes, hypertension or other afflictions. Thehealth outcomes can thus be numerical and continuous in nature (e.g.,health care cost) or categorical (e.g., number of medical visits,prescriptions, sick days).

Accordingly, in one implementation, the health scoring component 140utilizes health outcome logic 142 generates a health outcome score 165that is specific to a particular health outcome definition 155. Thehealth outcome logic 142 can be implemented as a formula or model, andcan take into account parameters that include the health parameter value151 determined an answered question, the number of questions answered,the time of involvement, etc. In one implementation, the healthparameter values 151 that can be combined or tabulated can be determinedfrom identifying the health questions 141 and responses 143 of the user.Based on the question and response the health correlative parameters 151are retrieved.

In an embodiment, the health scoring component 140 uses the healthcorrelation parameter 151, as well as the question 141 and response 143to predict the health outcome 165 of the user. In determining the healthoutcome, the health scoring component 140 can use a model or formula todetermine the health output score 165. For example, the health scoringcomponent 140 can map the user's input to a health score output which isthen predictive for the user. The model used by the scoring component140 to predict the health outcome score 165 of the user can be the samemodel which determines the correlation of questions to the particularhealth outcome definition. Examples of such models is provided with FIG.2.

The health outcome score 165 can be generated and stored as part of theuser health data store 160. Additionally, the health outcome score 165can be specific to a particular health outcome, and the type of value itreflects can be specific to the health outcome type. For example, oneimplementation provides that for a health outcome that reflects healthcare cost for the individual, the health outcome score 165 can benumeric indication of a specific cost or range of costs for theindividual. The health outcome score 165 for the number of medicalfacility visits, on the other hand, can be reflected by a category orlevel (e.g., 1 to 5 depending on amount).

In one implementation, the user health data store 160 is maintainedlogically or physically separate from the question response data store118 in order to preclude its viewability to users of system 100. Eachuser can include a profile of health outcome scores with the user healthdata store 160, with individual user profiles 141 which include scoresfor multiple different health outcomes. In some variations, a combinedscore or category may also be given to individual users as part of theirhealth profile.

As described with other embodiments, the health outcome score(s) 165 ofthe user can be made available for health services, such as healthinsurance services. For example, the premium, deductible or scope ofcoverage provided as part of a health insurance package for a user canbe determined from the health outcome score(s) 165. As another feature,health outcome score(s) 165 of the user can be used to determine if theuser should receive a discount for health insurance, or alternativelyreceive an added benefit from health related services that are provided(or are to be provided) to the user.

According to one embodiment, a health service 190 sub-system can utilizethe health outcome scores 165 provided in the user health database 160to determine designations, qualifications or service level, inconnection with a health-related service. Examples of health relatedservices 190 include health insurance, life insurance, health serviceplans, memberships in health related facilities (e.g., health spas,medical office), informational services (e.g., magazine or journalsubscriptions, electronic news). The benefit that can be provided to theuser includes the service itself, or alternatively a designation ofhealth for use with such a service. For example, the user's predictedlevel of health can be determined by the health outcome score(s) 165,and this can result in an overall health outcome determination (e.g., aranking or classification), which in turn can be used to receive adiscount for health related services (e.g., discount on health or lifeinsurance premium, expanded coverage, etc.). An example of healthservice sub-system is provided with an example of FIG. 6B.

In some implementations, the user interface 110 of system 100 caninclude various layers or functional components for enhancing theinteractivity level of the user. In one implementation, the userinterface 110 includes a gamification layer 112 and a community socialnetwork layer 114. The gamification layer 112 includes logic, data, andcontent (collectively “game data 103”) for implementing a competitiveenvironment for which the individual is to supply answers for questions127. The game data 103 can be generated a gaming engine 115, which canfurther personalize the gaming environment for the specific user. Forexample, the user identifier 121 can be used by a gaming engine 115 togenerate user-specific game data 103. The game data 103 can, forexample, include a competitive environment that is based on theknowledge level of the user and/or topical interests of the user. Animplementation that utilizes a gamification layer 112 is described withFIG. 7A. The gamification layer 112 can determine awards or credentials(e.g., skill level badges) for the user based on their performance. Byway of example, the questions 127 presented through the user interface110 can be associated with a score value that accounts for difficulty(which may be determined from a calibration process, as detailed below),response time, handicaps (e.g., the age of the user), etc.

The community social network layer 114 can operate using community data117, which can be generated from a community/social network service 116.The community/social network service 116 can, for example, provideuser-specific community (or social network) data based on the useridentifier 121. The community data 117 can provide content (e.g., user'shealth interest or knowledge specialties) that is provided as part ofthe community social network layer 114.

The health parameter value 151 represents a correlative and quantifiedmeasure as between human health and knowledge of a particular assertion.The granularity of the health parameter value 151 is applied to aquestion as answered from an individual, but the determination of thevalue can be based on a correlative model applied to a controlpopulation of users. The control population of users include thoseindividuals who, for example, voluntarily provide real-world informationabout themselves, and more specifically, actual health outcomes in arecent duration of time.

In more detail, system 100 can include a question analysis sub-system170 that includes functionality for determining correlations betweenknowledge of individual questions and actual health outcomes. Thesub-system 170 can implement and develop one or more correlative models172, which can analyze input questions 171 for purpose of determiningcorrelations to health outcomes. In particular, the correlative modelscan be implemented for purpose of determining health parameter values149 that statistically reflect a correlation as between knowledge ofindividuals in the control population (shown with the control populationdata store 180) for particular question and the respective healthoutcomes for those individuals who answered the question (eithercorrectly or incorrectly, depending on implementation). The healthcorrelative values 151 can be specific to individual questions orcluster of questions. In one implementation, different correlativemodels 172 can be used for different types of health outcomes. Differentcorrelative models may compare a predicted value with actual (orreal-world) data provided for individuals (shown as verified input 175).An example of question analysis sub-system is described in more detailwith an example of FIG. 2.

While numerous examples provide for use of health correlative scores,other embodiments can also generate recommendations to users based ontheir overall knowledge level, as determined by, for example, the user'sscore, or topic-specific scores. A response analysis 164 can retrievescores 145 from the response database 118, for example, and generaterecommendations, content or other output based on the user scores. Anexample of response analysis 164 is illustrated with FIG. 7B, andaccompanying examples thereof.

As an addition or alternative, the community social network layer 114can provide forums such as message boards, ask an expert, or topicalwalls for shared information and experiences. In one implementation,credentials that the user earns through the gamification layer 112 arecarried onto the social environment of the community social networklayer 114. For example, an ‘expert level’ user may have credence whenresponding to questions of others, even to a point where the user canrequest payment or other consideration for providing answers orinformation to other users.

FIG. 2 illustrates an analysis system, according to an embodiment. Inparticular, FIG. 2 illustrates an analysis system 200 for analyzingquestions (or other forms of assertions) for purpose of determiningwhether knowledge of the underlying assertions by subjects can becorrelated to physiological or mental health of the subjects. Accordingto some embodiments, individual questions, or alternatively groups ofquestions, can be correlated to a quantifiable metric that statisticallyrelates a subject's knowledge (or lack of knowledge) for an underlyingassertion to a likelihood of a particular health outcome. The system 200can be implemented as, for example, a sub-system of aphysiological/mental predictive system 100, such as shown with anexample of FIG. 1.

In more detail, system 200 includes a question intake interface 210, afielder 220, a calibration component 230, and a correlative modelimplementation component 250. A question interface can receive questions209 through, for example, a manual interface (e.g., experts generatequestions based on health assertions). The questions 209 can be manuallyassociated with one or more topics relating to human health, such astopics relating to nutrition or exercise, or specific medicalconditions. The granularity of the topics 211 can be determined byimplementation. A question store 215 can be used to store a question 209for processing as the question is calibrated and/or correlated to humanhealth.

The fielder 220 includes functionality to distribute the questions 209to a control population of users through a population interface 222. Forexample, the fielder 220 can issue questions using the user interface110 of an example system of FIG. 1. For example, with further referenceto an example of FIG. 1, questions 209 can be issued through gameplay ofuser interface 110, and responses from various users can be recorded.Some users, however, can be designated as belonging to the controlgroup. These users can correspond to individuals for which datacorresponding to ground truth data exists. For example, many users canbe given an opportunity to volunteer real-world health information. Suchusers can be asked questions such as “how many doctor visits did youhave last year” or “how many sick days did you have last year.” Stillfurther, some information like the user's health insurance cost can beobtained from a source such as the insurance companies. Accordingly, inone example such as shown by FIG. 1, members of the control group cansupply responses 213 to questions 209, presented through a game. At aseparate time, either before or after the questions 209 is presented tothe subject, the subject can also be given the choice to provide actualdata, shown as true user data 241. The true user data 241 can representan actual health outcome of a subject providing the response 213. Thetrue user data 241 can include information manually supplied by thesubject, as well as information provided by, for example, an insurancecarrier of the subject. Each response 213 from one of the subjects ofthe control population (e.g., those users of system 100 who opt-in toprovide information) can be linked to the question and to the identifier205 of the subject. Additionally, the true user data 241 can be linkedto the user identifier 205 of the subject providing the response.

The calibration component 230 can analyze the questions 209 underprocess to determine a difficulty level 265 of the question. Forexample, the calibration component 230 can query 231 the intake store215 for a tally of the number of responses which were correct andincorrect. The percentage of individuals who correctly answer a questioncan provide a basis for determining a difficulty level of the question.The difficulty level 265 can be stored with the question for subsequentuse.

The correlation model 250 operates to determine a correlation betweenknowledge by a subject for an underlying assertion of a question and thesubject's health. In one implementation, the correlation model 250implements one or multiple models for purpose of determining differentparametric values that statistically correlate to different healthoutcome definitions (e.g., amount of healthcare or healthcare cost anindividual requires, the number of medical facility visits, propensityfor heart disease, cancer, hypertension or diabetes, etc.). Thecorrelation model 250 can receive, as model input 255, (i) a questionidentifier 261, (ii) identification of a set of individuals in thecontrol group who answered the question 209, including identification ofthe answer each person provided to the question 209, and (iii) true userdata 241 for each person in the set of individuals that answered thequestion. The particular model selected compares an expected result to atrue result by (i) assigning the person to an expected result,corresponding to a particular health parameter value, based on theiranswer to a question, then (ii) using the true user data 241 to comparea true health outcome (reflecting real-world data of the individualsupplying the answer) to the expected result.

The expected result can initially start as a hypothetical or neutralvalue, indicating a likelihood that a given person has or does not havea particular health outcome based on the answer the person provided tothe question. The expected result can further include different valuesdepending on whether the user provided a correct answer or incorrectanswer, as well as which incorrect answer the user provided. The initialcorrelation can correspond to a coefficient (e.g., a value between 0and 1) that is set by, for example, an expectation as to whether theunderlying assertion of the question is information that is indicativeof health-conscious behavior (e.g., rubbing one's eyes can make a personsusceptible to common cold) or information that is indicative of poorhealth-conscious behavior (e.g., specific nutritional information abouta donut). From the initial value, the correlation can become positive,negative or made neutral based on the expected/actual comparison forpersons in the set. As more individuals are added to the set, thecorrelation can be made more valid or certain. The determinedcorrelation from the correlation model 250 can be identified ascorrelative health parameter 251. The correlative health parameter 251can be specific to a particular health outcome 253. The correlativehealth parameter 251 can, for example, correspond to a parametric value,such as a weight or coefficient, which can be aggregated, modeled and/orcombined with other parametric values to make a health outcomedetermination.

The particular model 255 implemented by correlation model 250 can dependon the nature of the health outcome that the assertion is to apply to.For a health outcome definition in which the health parameter value iscontinuous (e.g., monetary cost for health care in a given period,weight or body mass index), a linear regression model can be used. Somehealth outcome definitions can utilize health parameter values which aretiered or categorical. For example, the number of medical facilityvisits can be defined into tiered values, such as: 0=no medical facilityvisits, 1=1-2 medical facility visits in a year, 2=3-5 medical facilityvisits in a year, or 4=5 or more medical facility visits in a year.Similar tiered values can be used for health outcomes such as sick days.For such health outcomes, an ordinal logistic regression model can beused. In variations, a multinomial or polynomial model can be used fortiered categories, particularly those health outcomes which define tierswhich are not naturally ordered. Each question can be assigned to aparticular health outcome, so that the health parameter value isspecific to the determination of the health outcome.

Numerous other machine-learning models can be used in both developingcorrelative health parameters, and determining health outcomes based oncorrelative health parameters. By way of example, such machine-learningmodels can include random forest, neural network and/or gradientboosting models.

In some embodiments, the determination of the health parameter values251 can be tuned to reflect determinations that are for use with a modelin which no user-specific information is known. In one implementation,the control population can be associated with classification parameters,such as age group (e.g., over 50, under 50), gender, weight, race,geographic location or setting, and/or presence of certain medicalconditions such as diabetes. An individual question can be associatedwith multiple correlative health parameter values 251, including healthparameter values that reflect the general control population, as well asa health parameter value that is specific to a class or sub-class (e.g.,females over 50).

According to some embodiments, a combination of question and correlativehealth values 251 can map to one of multiple possible health outcomes.Thus, in one implementation, a question can have a correlative healthvalue as it applies to a single health outcome.

Other implementations provide for the determination of health parametervalues 251 which are correlative to health of a user based on a model inwhich a classification (e.g., gender or age) or set of classifications(e.g., gender and age) are known about the person answering thequestion. Depending on implementation, the classifications of users caninclude (i) unknown users, for which no information is known, (ii) usersfor which some basic health-relevant characteristic is known, such asage, gender, or combination thereof, (iii) users for which multiplerelevant facets of health is known, such as their weight and/or height,as well as, as gender and age. One implementation provides for thedetermination of correlative health parameters 251 which are determinedspecific for different classifications of the user, based on applyingmodels as described to segments of the control population which have therelevant classification. Thus, in some variations, the correlativehealth parameter values 251 can be made specific to specific classes ofpersons, so that the evaluation of health for the user is made inreference to the user's class. For example, in some embodiments, thequestions can be fielded for individuals who categorize themselves bygender, age, weight, and/or presence of certain medical conditions suchas diabetes.

System 200 can be implemented on a control group that is dynamic,meaning individuals can be added to the control group continuously overtime. As mentioned, a larger control group can provide more validresults. In an interactive gaming environment, such as described with anexample of FIG. 1, additional persons can be added to the control groupcontinuously through invitation or opt-in features. For example, theuser-interface 110 can prompt individuals to volunteer for questionsthat reflect actual medically relevant information. This mechanism canprovide a way to expand the control group with the addition of users forwhom true user data 241 can be provided. The control group can also bemanaged based on criteria, such as gender and age, so that it accuratelyreflects a desired population segment.

With the determination of the health parameter values 251, the questionscan be deemed processed, in which case the questions can be included ina library or collection of questions and marked as being correlative tohealth. In one implementation, a library build process 260 linkprocessed questions 259 with the question identifier 261, topicalidentifier, the difficulty level 265 and the correlative healthparameter 251 (or multiple values). The difficulty level 265 can be usedto determine which individuals receive the question based on user level.

While an example of FIG. 2 provides for processing of questions whichare deemed correlative to health, a fielding and calibration process canbe used to determine difficulty of all questions, including thosequestions which have no determined correlation to health. For example,any question can be associated with the topic 211 and fielded to thecontrol population as described, and further evaluated for difficultylevel 265 based on, for example, the percentage of individuals of thecontrol group who correctly answered the question.

FIG. 3 illustrates an example of a data structure that can be developedto link a question with a health outcome and a topic, according to oneor more embodiments. While an example of FIG. 3 illustrates the datastructure 300 as being logically integrated, variations can provide fordistributed data structures which associate or link parameters asdescribed. With reference to an example of FIG. 1, the data structure300 of an example of FIG. 3 can, for example, be provided with thequestion library 152, and include information provided with the healthscoring database 150.

In more detail, data structure 300 associates individual questions byquestion identifier 301 to one of multiple possible correlative healthparameters 303, and one or more topics 305. Other information orparameters that can be conveyed with the data structure 300 include adifficulty level, which can be determined, for example, through anoutput of the calibration component 230 (see FIG. 2). For a givenimplementation, the correlative health parameter can relate to aparticular health outcome. Multiple health outcomes can be defined for afuture time interval, including health care cost, medical facilityvisits, sick days, and number of prescriptions. Other examples of healthoutcomes include blood sugar level, weight or body fat (e.g., BMI),cholesterol level, depression or anxiety disorder, and/or longevity. Inone embodiment, each question associated to only one health outcome, andis further assigned a correlative health parameter value that reflects acorrelative measure between knowledge of the underlying assertion and acorresponding health outcome. In one implementation, a system of FIG. 2determines health parameter values for each defined health outcome, andthe health parameter value selected for a question is that which has thestrongest correlation. If no correlative health determination has beenmade for a question, then the health parameter values for such questionscan be shown as null.

As further shown by an example of FIG. 3, each question can be linkedwith multiple topics based on, for example, manual input. The determineddifficulty can also be expressed as a parameter, such as a numberbetween 0 and 1. The difficulty level can be independent of the topicassignment for the question-thus, meaning the difficulty level of aquestion can be provided as being the same regardless of the assignedtopic being considered.

Methodology

FIG. 4 illustrates an example method for predicting a health outcome ofa user based in part on whether a user has independent knowledge of anassertion relating to health. FIG. 5 illustrates an example method forpredicting a health outcome of a user based on a knowledge profile of auser. In describing example methods of FIG. 4 and FIG. 5, reference maybe made to elements of FIG. 1, FIG. 2 or FIG. 3 for purpose ofillustrating a suitable component for performing a step or sub-stepbeing described.

With reference to an example of FIG. 4, a collection of assertionsrelating to human health can be stored and processed for use with apopulation of users (410). In one implementation, the assertions can beformatted as questions for which the answer from the user indicateswhether the user has knowledge of the assertion (412).

For the control population, a health parameter value is determined forindividuals of the control population (420). The health parameter valuecan reference actual or real-world data which serves as an indicator ofphysiological or mental health of a user. In one implementation, thedetermination of the health parameter value can be based on input of auser. For example, in an interactive gaming environment of FIG. 1, someusers can opt-in to provide requested health-specific input, such as thenumber of sick days taken in the prior month or year. In someembodiments, the health parameter value is based on a defined healthoutcome (422), or combinations of health outcomes. By way of example,the health outcome can correspond to an estimated health care cost foran individual (424), a number of medical center visits for an individualin a given duration of time (425), a number of prescriptions for theindividual in the given time frame (426), and a number of sick days anindividual incurred in the given duration of time (428).

For each assertion, a correlative health parameter is determined (430).Generally, the correlative health parameter corresponds to a parametricmeasure that quantifiably links knowledge of an assertion to humanhealth. The health parameter value 151 (FIG. 1), 251 (FIG. 2), asdescribed with other examples, provides an example based on use of acontrol group (432).

The establishment of questions with associated correlative healthparameters can be done through implementation of a model, with groundtruth data provided by select users from a larger user base ofrespondents. Once the correlative health parameters are established forindividual questions, the questions can be fielded to the user base. Theresponses from the user can be used to determine the user's independentknowledge level of a particular assertion (440).

The correlative health parameters for the individual questions answeredby the user can be determined and modeled into a value for a particularhealth outcome (450). For each user, the correlative health parametersof the answered questions pertaining to a particular health outcome canserve as inputs in order to determine a predicted health outcome for theuser (460). Multiple health outcomes can be determined in this manner.

With reference to FIG. 5, a knowledge profile of a user can bedetermined, relating to a particular health outcome (510). The knowledgeprofile can reflect answers to individual questions, or answers toclusters or groups of questions. The knowledge profile can be determinedbased on a selected definition. In one implementation, the knowledgeprofile is specific to a question, and reflects whether a user correctlyanswer the question. In a variation, the knowledge profile is specificto a question, and reflects which question the user answered. Stillfurther, the knowledge profile can reflect the user's answers inaggregate form, such as in a cluster of questions (e.g., 3 to 10questions), reflecting facets such as the number of questions the usercorrectly answered in the cluster, or the number of answers providedwhich were deemed more wrong than others.

A facet of the knowledge profile can be compared to corresponding facetsof knowledge profiles from individuals of a control group (520). In oneimplementation, the user's answer to a particular set of questions canbe individually compared to an answer to the same set of questions fromone or multiple persons of the control group. In variations, the user'sanswer to a cluster of questions can be compared to answers provided bya subset of the control group for the same cluster of questions, withthe comparison being made for the cluster of questions as a whole. Stillfurther, the user's answers can be compared to answers provided by asubset of the control group which provided the same exact answers forthe cluster of questions.

A health outcome can be determined for individuals of the control group(530). As mentioned with other examples, the health outcome can bedefined as a healthy living style characteristic that is indicative ofhuman health. The health outcome that is determined for a person of thecontrol group can reflect real-world information about that person(532). In one implementation, individuals of the control group canvolunteer their personal health outcome information (534). For example,the information can be provided in exchange for some benefit to theperson of the control group. In variations, the health outcomesinformation for persons of the control group can be determined fromsources such as health care or insurance providers (536).

The health outcome of a user can be predicted based in part on acorrelation between the health outcomes of individuals in the controlpopulation and the compared facets of the knowledge profile between theuser and persons of the control group (540). Thus for example, a user'sanswer to individual questions can be compared to the answers providedfor the same questions by those members of the control group. As anaddition or alternative, a user's answers to a cluster of questions canbe compared to answers provided to the same cluster of questions forindividuals of the control group, with, for example, the comparisonbeing based on matching the user with a subset of persons of the controlgroup based on a percentage of correct or incorrect answers provided.

Health Service Methodology and Sub-System

FIG. 6A illustrates an example method for providing a health relatedservice to a user based on a knowledge-predicted health outcome for auser. In describing example method of FIG. 6A, reference may be made toelements of FIG. 1, FIG. 2 or FIG. 3 for purpose of illustrating asuitable component for performing a step or sub-step being described.

With reference to FIG. 6A, a health knowledge profile is determined fromeach of multiple users (610) with regard to assertions relating tohealth (e.g., physiological or mental health). As mentioned with otherexamples, the health knowledge profile can reflect individual answers toquestions, those questions which were answered correctly or incorrectly,specific answers provided to specific questions (e.g., such as incorrectanswers), and/or percentages of questions answered from a definedcluster of questions.

Additionally, as mentioned with other examples, a value of a healthcorrelation parameter can be determined as between the user and a subsetof persons in the control group (620). With reference to an example ofFIG. 1, the health value parameter 151 can, for example, be determinedby the health scoring component 140. In determining the healthcorrelation parameter, a given facet of the users' knowledge profile canbe compared to that of relevant persons in the control group (622). Byway of example, the comparison can be on a question by question basis,or alternatively, on a cluster basis (e.g., compare set of 5 answers,etc.). Actual health outcomes can be known for members of the controlgroup, and the identified correlative health parameters can be based inpart on the known health parameters of individuals in the control group.The correlative health parameter can thus be pre-determined for thecontrol group, and based on real-world information about members of thecontrol group.

Based on the correlation values, a health outcome determination isprovided for the user (630). As shown with an example of FIG. 3, thecorrelation values can be specific to pre-determined health outcomes.Further with reference to an example of FIG. 1, given a set of healthparameter values 151 for a particular health outcome, the health scoringcomponent 140 can make a health outcome determination. The determinationof the health outcome can be in the form of a score, so that it gives arelative measure of the particular health outcome as compared to otherindividuals in the general population. The health outcome determinationcan correspond to a health outcome score 165, or alternatively, to acombination of health outcome scores. For example, multiple healthoutcome scores can be determined for the user, and the scores can becombined to form an aggregate health outcome determination.

Based on the health outcome determination, a health service benefit canbe provided to the user (640). The service or designation can be onemade for a set value, wherein the service or designation is associatedwith a true per-user cost that is not equal to the set value, but whichis variable and set to increases over time when individual users in thesubset suffer negative health consequences as a result of a naturallyprogressing medical condition.

The health service benefit can correspond to a variety of direct andindirect service related benefits. In one implementation, those userswith a health outcome determination that exceeds a particular thresholdcan receive a designation (642). The designation can correspond to aservice or credential provided to only select users of, for example, anetwork service provided with system 100 (644). For example, those userswhich receive a health outcome determination that places them within thetop 10 percentile of all users may receive a certification, which inturn enables them to receiving discounts with their healthcare provider,health insurance, or related health service activities (e.g., discountwith nutrition store, athletic gym membership, life insurance, etc.).Alternatively, the designation can entitled the subset of users toreceive a service, such as primary health insurance, supplementalaccidental insurance, life insurance, or other membership service(whether health related or not).

In variations, the health outcome determination provides a basis forpredicting a user's health, and this basis can in turn be used todetermine health related services for the user (646). For example,health insurance, life insurance, and/or accidental health insurance canbe provided to the user with scope and cost determined by the healthoutcome determination. For example, the cost of the premium ordeductible to the individual user can be based on the health outcomedetermination (648). By way of example, an insurance service can beprovided to users of system 100, and those users with better healthoutcome determinations can be provided discounts to their premiums ordeductibles, or alternatively given greater scope of coverage ascompared to counterparts users who have lesser health outcomedeterminations.

FIG. 6B illustrates a health service sub-system 680, according to anembodiment. A health service sub-system 680 can be implemented with oras part of, for example, system 100. In variations, the health servicesub-system 680 can be provided as a separate system which interfaceswith the system 100. Additionally, the health service sub-system 680provides an example of a system on which an example of FIG. 6 can beimplemented.

With reference to FIG. 6B, a health service sub-system 680 includes asystem interface 682, a customer data store 684, and servicedetermination logic 686. The health service sub-system 680 can alsoinclude a service customer interface 688, such as a web page orapplication page, which a service customer accesses to provide input fordefining the health service offered, as well specific logic orparameters for the service determination logic 686. The service customerinput 685 can, for example, include text data definition of the serviceoffered (e.g., terms of health or life insurance), as well asupplemental content for viewing by users of system 100. This input canbe stored in the service data store 684.

In some variations, the service customer input 685 can further inputparameters 683 and other logic (e.g., rules) for the servicedetermination logic 686. The parameters 683 and rules can, for example,including definition of the qualifications needed for users to (i)receive the service, (ii) receive a particular facet or tier of theservice, and/or (iii) receive the service or tier according to aparticular price structure. For example, the service can include tiersof benefits, or multi-tiered cost structure, and each tier can beprovided to users based on qualifications, such as one or more of (i) athreshold health outcome score or set of scores, (ii) a thresholdcombination of health outcome score, and/or (iii) other health outcomedetermination.

The system interface 682 can interface with the user health database 680in order to determine the health outcome scores 689 of a given user oruser-base. In a variation, the system interface 682 can communicate witha push or trigger component on the system 100 which in turn retrievesand pushes specified health outcome scores to the system interface 682.In some embodiments, end-users are precluded from handling healthoutcome data. The output of health determination logic 686 cancorrespond to a notification 691, which can specify the results of thehealth determination logic 686. These results can be communicated toeither the user or to a provider of the health service benefit.

Game Play

Numerous embodiments described use of game play and logic as a mechanismto increase use response and participation. More user response andparticipation can have numerous benefits, including (i) increasing thesize of the control group, by finding more qualified volunteers who arewilling to provide real-world health information for purpose ofdeveloping health correlations to questions, (ii) more predictivecorrelations based on larger statistical sample, and (iii) data pointsfrom users, enabling better prediction of individual user health.Additionally, the use of game logic provides a mechanism to hide healthcorrelative questions from public inspection, thereby precluding usersfrom “gaming” the questions (e.g., studying) for purpose of receiving agood health score.

FIG. 7A illustrates an example method for providing a game-basedenvironment in which user responses enable prediction of health outcomesfor individual users. In describing an example method of FIG. 7A,reference may be made to elements of FIG. 1, FIG. 2 or FIG. 3 forpurpose of illustrating a suitable component for performing a step orsub-step being described.

With reference to an example of FIG. 7A, a set of questions can bestored, were at least some of the questions are based on assertions thatare core relative to health (702). For example, questions can be storedin the question library 152, after being processed using a system suchas described with an example of FIG. 2. The stored questions can includeboth (i) health correlative questions, which are used in determining ahealth outcome score or determination for the user (704); and (ii)non-health correlative questions. While the latter questions may pertainto health, those questions have either not been determined to becorrelative or health, or those questions have little relevance toawareness for health, and thus correlative to actual human health (706).As mentioned with other examples, a gaming environment can beimplemented in which the questions are provided as trivia, so that usersreceive entertainment benefit from participating in answering questions.

Still further, as described with other examples, the health correlativequestions can be process to determine a health correlative parameter(710). For example, question analysis subsystem 200 can be used todetermine a health correlative parameter 151 for a given question. Stillfurther, as described with other examples, the health correlativeparameter can be based on persons in the control population who haveknowledge (or knowledge deficit thereof) of an assertion underlying theparticular question (712).

In order to encourage participation and development of accurate healthoutcome scores and determinations, a gaming environment can beestablished in which users are asked questions in a competitive orsemi-competitive context (720). An example of a gaming environment isshown with environments depicted through interfaces of FIG. 8A through8H.

The user responses to trivia questions are recorded, with thoseresponses include both scores related to health correlative questions(730) and scores related to all questions (or alternatively tonon-health correlative scores) (732). As described with an example ofFIG. 6, the health correlative questions can be scored for purpose ofdetermining health services to the user (740). This score may be hiddenor unknown to the user, and determine independently of the overallgaming score.

Conversely, the overall gaming score can be published in a social orgaming environment, to provide the user with credentials in thecommunity of the service provided through system 100 (742). For example,the user can use the latter gaming score to achieve credentials thatgive the user authority on message board discussions, and question andanswer forums of the community platform.

In some variations, the gaming score can also provide a mechanism toprovide health base recommendations to the user (744). For example, theuser's knowledge base can be evaluated based on topical subjects, andthe user's deficiency or strengths respect to specific topics of healthcan be used to infer physiological or mental information about the user.

FIG. 7B illustrates a knowledge-based recommendation engine, accordingto one or more embodiments. With reference to FIG. 1 and FIG. 7B, forexample, the response analysis component 164 can include recommendationengine 780. The recommendation engine 780 can use information about theuser's knowledge in order to generate recommendations 785, which caninclude content that communicates to the user specific actions,lifestyle choices, or areas of growth (for knowledge or lifestyle), forpurpose of growth.

In one implementation, the recommendations 785 can be based on thedeterminations of the user's strength or weakness with regards tospecific topics of health. The recommendation engine 780 can includeprocesses 782 which retrieve the user's topical scores 781, and thencorrelate the topical scores with recommendation logic 790. Therecommendation logic 790 can include rules 791, 793 for selectingrecommendations for the user based on different topical scores andcriteria. For example, the recommendation logic 790 can include rulesfor suggesting recommendations to users for specific topics when theuser's score for the topic is below a threshold. By way of example, atopic can be defined for cardiac health, and anytime a user's topicalscore for cardiac health is below a threshold, a set of recommendations785 for improving the user's cardiac health can be generated andcommunicated to the user. Likewise, if the user's knowledge is strong ina particular topic, that can also be interpreted as interest, and therecommendation logic 790 can utilize the score to suggestrecommendations that are of an advanced level. For example, if the userscores high in the topic of weight lifting, then the recommendationprovided to the user can include specific techniques or recommendationsbased on questions that have the highest difficulty level (as determinedfrom, for example, a calibration component 230 of FIG. 2).

In a variation, the set of recommendations 785 generated for any onetopic can be associated linked with questions or sub-topics ofquestions. A recommendation filter 792 can filter the recommendations785, so as to weed out those recommendations the user likely knows basedon their correctly answered questions.

Still further, the recommendation logic 790 can include combinationrules, which select recommendations 785 for the user based on criterionprovided by the user's topical score in two or more topics. Thecombination rules can identify subject matter relevancy between topics,so that the user's knowledge of one topic will benefit another or viceversa. In one implementation, when the user's topical score of one topicexceeds a threshold, and the topical score of another topic is below athreshold, then the recommendation may be provided that assumes useractivity or interest in one topic to assist the user's knowledge orlifestyle with regards to the second topic. For example, the user mayhave scored high in the topic of weight-lifting, but scored low innutrition or sleep. The recommendation provided to the user may identifythe recommended hours for the user to sleep in order to add muscle mass.

By way of another example, if the user is strong on a subject such asweight training, but poor in nutrition, then the recommendation enginecan suggest (i) that the user develop his knowledge on nutrition, (ii)identify nutritional information related to a training in order toprovide recommendations. Recommendations can include, for example, withthe user should eat when we training, house such nutritional intake canaffect performance in retraining, recommendation for the user to confirmwith a nutritionist, expected results that can be achieved throughproper diet and weight training. Such an example illustratesrecommendations that can be made based on the user being strong in hisor her knowledge base for one topic an week in another topic. In suchscenarios, the relationship between the two topics can be determined inorder to generate programmatically actions and subtopics of learningwhich may be of interest or benefit to the user.

Similar recommendations can be determined and linked to user's topicalscores based on different threshold determinations. In oneimplementation, if the user scores low on two topics related by subjectmatter, the user's recommendation may be selected on the assumption thatthe user suffers from health consequences related to a physiological ormental problem related to the topics.

Still further, analysis of the topical determinations can also be usedto infer characteristics about the respondents, without any mathematicalcorrelation being made to the control population. For example, anindividual who scores poorly in both nutrition and exercise can beinferred to be obese, potentially diabetic, and/or suffer from otherhealth related issues such as depression. Based on such analysis, therecommendation engine can suggest areas of growth for the user'sknowledge. The recommendation engine 780 can also provide recommendedactions, such as publishing a diet to the user for weight loss,suggesting the user visits a psychiatrist (on a sound assumption thatthe user is depressed), suggesting the user sees a nutritionist and/orpersonal trainer (on the side assumption that the user is overweight),or recommend that the user have his blood sugar checked for diabetes andor high cholesterol. Such actions can follow when the user scores poorlyon knowledge in topics that have synergy or relation to one another whenconsidered for physiological or mental health.

Example Interfaces

FIG. 8A through 8H illustrate example interfaces for use with one ormore embodiments described herein. Interfaces such as described withFIGS. 8A through 8H can be implemented using, for example, a system suchas described with an example of FIG. 1. Accordingly, reference may bemade to elements of FIG. 1 for purpose of illustrating suitablecomponents for implementing an interface as described.

In FIG. 8A, in interface 800 provides a topical selection 804 for a user(e.g., nutrition). The interface 800 can be displayed with informationfrom the user's profile 138, such as their game score 802 (e.g.,provided as game data 119 of the user's profile, in an example ofFIG. 1) and badges or certifications 805.

The panel 810 of FIG. 8B illustrates a question 812, in the form oftrivia. A set of answers 814 can be provided to the user, from which theuser can make selection of in order to affect his or her score.

FIG. 8C illustrates a panel 820 that provides feedback 825 to the useras to the correctness of the answer, as well as supplemental informationregarding the correct answer and/or assertion underlying the question.In FIG. 8D, once the user provides the answer; the user can be providedan additional panel 830, displaying the underlying assertion 832 behindthe question. Other information, such as the percentage of individualswho answer the question correctly can be displayed to the user. Thisfeature 834 can also reflect the difficulty level of the question.

FIG. 8E illustrates a panel 840 on which a menu of options is provided.The user can select from the menu of options. As shown, thefunctionality provided includes gaming (e.g., leader board) andcommunity interaction (e.g., discussions), in a gaming and socialenvironment such as described with an example of FIG. 1. Additionally,the menu of options can include a health report feature 842 that candisplay, for example, recommendations as determined from an example ofFIG. 7.

FIG. 8F illustrates a panel 850 that provides a gaming summary for theuser, displaying the user's overall score 852, as well as badges arehonors marking 854 achievements in the number of questions the useranswered etc.

FIG. 8G illustrates a panel 860 on which a leaderboard 862 is provided.The leaderboard can be topic specific and/or categorized by user level.

FIG. 8H illustrate the panel 870 for enabling social interaction, gamingand knowledge base forums through a system such as described with anexample of FIG. 1. Among other social interaction functions, one or moreknowledge base “twins” can be identified to the user. The twins cancorrespond to an individual who closely shares one or more of (i)knowledge profile about health, or certain topics of health with theuser, and/or (ii) similar or same health outcome values ordeterminations. As an addition or variation, the twin can also includesimilar demographic profile, such as having the same gender, age and/orrace. Identify twins can be shown to each other as a mechanism forbuilding social interaction and shared experiences, particularly as todistributing health-based knowledge, information and services.

Computer System

One or more embodiments described herein provide that methods,techniques and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmaticallymeans through the use of code, or computer-executable instructions. Aprogrammatically performed step may or may not be automatic.

One or more embodiments described herein may be implemented usingprogrammatic modules or components. A programmatic module or componentmay include a program, a subroutine, a portion of a program, or asoftware or a hardware component capable of performing one or morestated tasks or functions. As used herein, a module or component canexist on a hardware component independently of other modules orcomponents. Alternatively, a module or component can be a shared elementor process of other modules, programs or machines.

Furthermore, one or more embodiments described herein may be implementedthrough instructions that are executable by one or more processors.These instructions may be carried on a computer-readable medium.Machines shown or described with figures below provide examples ofprocessing resources and computer-readable mediums on which instructionsfor implementing embodiments of the invention can be carried and/orexecuted. In particular, the numerous machines shown with embodiments ofthe invention include processor(s) and various forms of memory forholding data and instructions. Examples of computer-readable mediumsinclude permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, such as CD or DVD units, flashor solid state memory (such as carried on many cell phones and consumerelectronic devices) and magnetic memory. Computers, terminals, networkenabled devices (e.g., mobile devices such as cell phones) are allexamples of machines and devices that utilize processors, memory, andinstructions stored on computer-readable mediums. Additionally,embodiments may be implemented in the form of computer-programs, or acomputer usable carrier medium capable of carrying such a program.

FIG. 9 is a block diagram that illustrates a computer system upon whichembodiments described herein may be implemented. For example, in thecontext of FIG. 1, FIG. 2, FIG. 6B and FIG. 7B, a network service orsystem can be implemented using one or more computer systems such asdescribed by FIG. 9. Still further, methods such as described with FIG.4, FIG. 5, FIG. 6A and FIG. 7A can be implemented using a computersystem such as described with an example of FIG. 9.

In an embodiment, computer system 900 includes processor 904, memory 906(including non-transitory memory), storage device, and communicationinterface 918. Computer system 900 includes at least one processor 904for processing information. Computer system 900 also includes a memory906, such as a random access memory (RAM) or other dynamic storagedevice, for storing information and instructions to be executed byprocessor 904. The memory 906 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 904. Computer system 900 mayalso include a read only memory (ROM) or other static storage device forstoring static information and instructions for processor 904. Thecommunication interface 918 may enable the computer system 900 tocommunicate with one or more networks through use of the network link920 (wireless or wireline).

In one implementation, memory 906 may store instructions forimplementing functionality such as described with example systems orsub-systems of FIG. 1, FIG. 2, FIG. 6B or FIG. 7B, or implementedthrough example methods such as described with FIG. 4, FIG. 5, FIG. 6Aor FIG. 7A. Likewise, the processor 904 may execute the instructions inproviding functionality as described with example systems or sub-systemsof FIG. 1, FIG. 2, FIG. 6B or FIG. 7B, or performing operations asdescribed with example methods of FIG. 4, FIG. 5, FIG. 6A or FIG. 7A.

Embodiments described herein are related to the use of computer system900 for implementing functionality as described herein. The memory 906,for example, can store a question library 931 (see, e.g., also questionlibrary 152 of FIG. 1), including values for health correlativeparameters 933 (see e.g., also health correlative parameters 151 ofFIG. 1) of the some questions. The memory 906 can also storeinstructions 941 for determining a health score, in order to determineone or more correlative health parameters for a user, in connection withthe user's participation of responding to questions in an interactivecommunity or game environment.

According to one embodiment, functionality such as described herein canbe performed by computer system 900 in response to processor 904executing one or more sequences of one or more instructions contained inthe memory 906. Such instructions may be read into memory 906 fromanother machine-readable medium, such as through a non-transitorystorage device. Execution of the sequences of instructions contained inmemory 906 causes processor 904 to perform the process steps describedherein. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions to implementembodiments described herein. Thus, embodiments described are notlimited to any specific combination of hardware circuitry and software.

Although illustrative embodiments have been described in detail hereinwith reference to the accompanying drawings, variations to specificembodiments and details are encompassed by this disclosure. It isintended that the scope of embodiments described herein be defined byclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described, either individually or as part of anembodiment, can be combined with other individually described features,or parts of other embodiments. Thus, absence of describing combinationsshould not preclude the inventor(s) from claiming rights to suchcombinations.

What is claimed is:
 1. A method for providing a service, the methodbeing implemented by one or more processors and comprising: storing acollection of questions, each question being based on a documentedassertion pertaining to human health; associating each question in afirst subset of the questions with a correlative health parameter thatis based at least in part on (i) persons in a control population of thathave independent knowledge of an assertion that is a basis of thatquestion, and (ii) a value of a predetermined health parameter for eachperson in the control population the value of the predetermined healthparameter for each person being indicative of that person's health;wherein the second subset of the questions is associated with a null orneutral correlative health parameter; displaying a corresponding set ofquestions from the collection for response for each user in the set ofusers; determining a response score for each user in the set of usersbased on a correctness of their respective reply to each question in thecorresponding set of questions; determining a health parameter value forat least a health outcome based at least in part on the correlativehealth parameter of at least some questions in the corresponding set ofquestions.
 2. The method of claim 1, wherein the health parameter valueis not visible to the user, while the response score is displayed to theuser.
 3. The method of claim 1, further comprising predicting a healthoutcome for the user based on the health parameter value.
 4. The methodof claim 1, further comprising: based on a user response to theindividual questions from the collection, determine a topic of healththat the user is relatively weak on with knowledge, and provideinformation for strengthening the user's knowledge on the topic.
 5. Themethod of claim 4, further comprising based on a user response to theindividual questions from the collection, determining a topical subjectmatter to advise the user on based on a topical response score of theuser being relatively high for a first topic and relatively low for asecond topic.
 6. The method of claim 4, further comprising based on auser response to the individual questions from the collection,determining a topical subject matter to advise the user on based on atopical response score of the user being either relatively high for eachof a first topic and a second topic or relatively low for each of thefirst topic and second topic.
 7. The method of claim 1, furthercomprising awarding the user with a credential for achieving a knowledgelevel in a particular category based on the user response score forquestions of the category.
 8. The method of claim 1, wherein displayingthe set of questions for the set of users includes providing an onlinemufti-user trivia game for the set of users.
 9. The method of claim 1,further comprising selecting to provide the user with a health servicebased at least in part on the health parameter value.
 10. The method ofclaim 1, wherein the health parameter value includes a categorydesignation.
 11. The method of claim 1, wherein the health parametervalue correlates to a monetary value.
 12. A method for providing healthrecommendations to a user, the method being implemented by one or moreprocessors and comprising: providing a plurality of questions to theuser, the plurality of questions including multiple questions for eachof multiple health-related topics, so that individual questions are eachassociated with one or more of the multiple topics; determining a scoreof the user answering each question in the plurality of questions,including determining a topical score for one or more of the multipletopics; based on the topical score of at least a first topic,identifying a set of recommendations for the user, the set ofrecommendations including an action that the user can perform to improvethe user's mental or physiological health relating to the topic.
 13. Themethod of claim 12, wherein identifying the set of recommendationsincludes pre-determining a collection of recommendations based on athreshold score of the user in answering questions of a correspondingtopic, then filtering the collection of recommendations based on thosequestions of the corresponding topic which the user correctly answered.14. The method of claim 12, wherein identifying the set ofrecommendations includes determining a set of recommendations based on ascore of the user in answering questions of at least two distincttopics.
 15. The method of claim 14, wherein determining the set ofrecommendations includes selecting the recommendations based on a scoreof the user in answering the questions of each of a first topic andsecond topic being relatively low as compared to a threshold, for whicha higher score would indicate more knowledge.
 16. The method of claim14, wherein determining the set of recommendations includes selectingthe recommendations based on each of (i) a score of the user inanswering questions of a first topic being relatively high, for which alower score would indicate less knowledge, and (ii) a score of the userin answering questions of a second topic being relatively low, for whicha higher score would indicate more knowledge.
 17. The method of claim12, wherein identifying the set of recommendations includes assuming amedical condition of the user based on a score of the user to questionsfor one or more of the topics.
 18. The method of claim 17, whereinidentifying the set of recommendations includes identifying arecommendation for the medical condition.
 19. The method of claim 1,wherein displaying the set of questions for the set of users includesproviding an online mufti-user trivia game for the set of users.
 20. Acomputer-readable medium that stores instructions, which when executedby one or more processors of a computer, cause the c to performoperations that comprise: storing a collection of questions, eachquestion being based on a documented assertion pertaining to humanhealth; associating each question in a first subset of the questionswith a correlative health parameter that is based at least in part on(i) persons in a control population of that have independent knowledgeof an assertion that is a basis of that question, and (ii) a value of apredetermined health parameter for each person in the control populationthe value of the predetermined health parameter for each person beingindicative of that person's health; wherein the second subset of thequestions is not associated with a null or neutral correlative healthparameter; displaying a corresponding set of questions from thecollection for response for each user in the set of users; determining aresponse score for each user in the set of users based on a correctnessof their respective reply to each question in the corresponding set ofquestions; determining a health parameter value for at least a healthoutcome based at least in part on the correlative health parameter of atleast some questions in the corresponding set of questions.